Genome Canada precision medicine strategy for structured national implementation of epitope matching in renal transplantation
Bibliographic record
Abstract
Advances in immunology support the understanding that precise structural epitopes on the antibody-accessible region of the HLA molecule determine antigenicity and challenge the need for identity across the full HLA molecule to minimize graft immunogenicity. Retrospective studies confirm that quantitative measurement of epitope-level mismatching between donor and recipient is an informative marker of graft rejection and survival and suggest that prospective allocation of donor organs based on this principle may improve graft survival. Here we describe the process for rigorous prospective evaluation of this hypothesis in a formal national proof-of-concept program for epitope-based matching. This encompasses broad societal consultation to engage the public, patients and providers; the development of clear allocation policies with strategies to support candidates who may be difficult to match; molecular and sequencing methods and web-based calculators enabling rapid epitope typing and recipient selection; precise immunological monitoring of the graft response; information systems permitting real-time monitoring of clinical outcomes; and assessment of health benefit and economic cost. The results of this objective evaluation can then be provided to payers and policy-makers for review, and adoption if of proven benefit.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".